Mass Cytometry Analysis Dissects CRLF2-Driven Signaling Pathways in Childhood B-Cell Precursor Acute Lymphoblastic Leukemia (BCP-ALL)

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 906-906
Author(s):  
Jolanda Sarno ◽  
Kara L. Davis ◽  
Angela Maria Savino ◽  
Cristina Bugarin ◽  
Stefania Pinto ◽  
...  
Keyword(s):  
Single Cell ◽  
Signaling Pathways ◽  
Cross Talk ◽  
Research Funding ◽  
Ex Vivo ◽  
Single Cells ◽  
Lymphoblastic Leukemia ◽  
Cell Mass ◽  
Control Group ◽  
Mass Cytometry

Abstract BACKGROUND: Rearrangements of the CRLF2 gene, present in 7-15% of childhood BCP-ALL, are responsible of the overexpression of Thymic Stromal Lymphopoietin Receptor (TSLPR) and they are correlated with poor prognosis (Chen IM Blood 2012). TSLPR overexpression can be associated with JAK2 mutations, which leads to aberrant activation of JAK/STAT and PI3K/AKT pathways. Although the cross talk of the signaling pathways is still under investigation, there is a rationale for the use of targeted tyrosine kinase inhibitors (TKIs) to treat this subgroup of patients (Maude SL Blood 2012). We focused on the dissection of CRLF2-driven signaling in primary CRLF2 rearranged(r) BCP-ALL samples by using single cell mass cytometry (CyTOF) analysis. We leveraged the high dimensional single cell capability of the CyTOF to understand, with previously unattainable resolution, the activation of these pathways simultaneously in single cells and their response to inhibition with TKIs and anti-TSLPR monoclonal antibodies (mAbs). This revealed heterogeneity in signaling response, identifying subpopulations which differentially activate intracellular signals through TSLPR and differentially respond to ex vivo treatment. METHODS: Twelve BCP-ALL primary samples, 6 CRLF2r and 6 CRLF2 wild type (wt), were investigated and the expression of 24 phenotypic and 15 functional proteins were measured at single cell level using CyTOF as previous described (Bendall SC Science 2011). To assess the response to ex-vivo TSLP stimulation (10ng/mL) and TKIs/mAbs treatment, data were normalized to the basal levels of each phosphoprotein and significance was calculated using student`s t-test. One million cells per condition were treated with different TKIs, Dasatinib, Ruxolitinib and BEZ-235, and two different clones of anti-TSLPR mAbs (130A10 and 130H3) from MRC Technology. RESULTS: As expected, we observed an aberrant TSLP-induced activation of pSTAT5 and prpS6 in CRLF2r patients as compared with CRLF2wt, used as control group (p=0.0055, p= 0.0007). Of note, we also observed a previously not described TSLP-dependent activation of pERK and pCREB (p=0.0313, p=0.0261) suggesting a cross-talk of the TSLPR-driven signaling also with the RAS/MEK pathway. Treatment with TKIs revealed strong inhibitory activity of Dasatinib, which completely inhibited the TSLP-mediated phosphorylation of STAT5, rpS6, CREB and ERK in CRLF2r treated blasts compared to CRLF2r not treated cells (p= 0.0040, 0.0017, 0.0007, 0.0114 respectively). Ruxolitinib, JAK1/2 inhibitor, also reduced rpS6, CREB and ERK phosphorylation (p=0.0025, 0.037, 0.0132). Interestingly one of the two anti-TSLPR tested mAbs (130A10) was also able to significantly inhibit the TSLP-mediated activation of STAT5, rpS6, and ERK (p= 0.0071, 0.0006, 0.0323). Finally, the PI3K/TORk inhibitor, BEZ-235, did not show any statistically significant reductions. Single cell analysis revealed a population of TSLPR overexpressing blasts (range 20-50%) in which the TSLP stimulation resulted in activation of prpS6 but not pSTAT5, present in all the CRLF2r patients. This rpS6 activation could be inhibited by anti-TSLPR mAb, Dasatinib, Ruxolitinib and BEZ-235, except for one patient in which the activation was blunted only by anti-TSLPR mAb and Dasatinib suggesting an activation of prpS6 through a non canonical pathway. This data reveals heterogeneous signaling populations present within this subtype of leukemia driven by TSLPR overexpression. Finally in 3 additional CRLF2r primary samples, we investigated signaling profile of residual blasts (MRD) at Day8 and Day15 post induction initiation. TSLPR expression was consistently maintained in all patients at both time points. Furthermore, residual blasts were still able to respond to TSLP and the induced pSTAT5 could be effectively inhibited by 130A10 anti-TSLPR clone and Ruxolitinib. CONCLUSION: In summary, these data suggest heterogeneity of TSLPR-related signaling with activation of the expected JAK/STAT and PI3K pathways but also RAS/MEK and CREB activation. Further, TSLPR+ blasts exhibit heterogeneous responses to both treatment with TSLP in combination with TKIs or mAb. Finally, the MRD detection by CyTOF allowed the study of the functional activity of the TSLPR positive resistant cells suggesting a role of CRLF2r in the persistence of the leukemic cells and its targeting to treat late and refractory stages of the disease. Disclosures Davis: Fluidigm, Inc: Honoraria. Dyer:Roche Pharmaceuticals: Speakers Bureau; Gilead: Research Funding; ONO Pharmaceuticals: Research Funding. Nolan:Fluidigm, Inc: Equity Ownership.

scholarly journals Impaired coordination between signaling pathways is revealed in human colorectal cancer using single-cell mass cytometry of archival tissue blocks

2016 ◽  
Vol 9 (449) ◽  
pp. rs11-rs11 ◽  
Author(s):  
A. J. Simmons ◽  
C. R. Scurrah ◽  
E. T. McKinley ◽  
C. A. Herring ◽  
J. M. Irish ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Single Cell ◽  
Signaling Pathways ◽  
Cell Mass ◽  
Mass Cytometry ◽  
Human Colorectal Cancer ◽  
Archival Tissue

All Roads Lead to Rome: Phenotypic Channeling to Uniform Differentiation Structures from Diverse Leukemia Genotypes by Mass Cytometry

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. SCI-22-SCI-22
Author(s):  
Garry Nolan
Keyword(s):  
Stem Cells ◽  
B Cell ◽  
Single Cell ◽  
Conflicts Of Interest ◽  
Single Cells ◽  
Lymphoblastic Leukemia ◽  
Myelogenous Leukemia ◽  
Signaling Networks ◽  
Mass Cytometry ◽  
Human Cord Blood

Abstract Emerging single-cell technologies have been pivotal in uncovering an extensive degree of heterogeneity between and within tissues (1). Analysis of single-cell data has shed light on many different cellular processes (2-7) and recent technological advances have enabled the study of a large number of parameters in single cells at unparalleled resolution. One such technology, mass cytometry (8), can measure up to 45 parameters simultaneously in tens of thousands of individual cells. Using mass cytometry and genomic sequencing of conventionally sorted subpopulations show that acute myelogenous leukemia (AML) in a given patient can simultaneously occupy multiple stages of differentiation. Occupation of these stages was correlated with the presence, or absence, of unique exonic mutation fingerprints. In another cancer, B-cell acute lymphoblastic leukemia (ALL), outgrowth of tumor at pro and pre-B cell stages was nearly always uniquely at a single stage - contrary to the results in AML. This suggests that evolutionary “niche” searching is not only for physical space in cancers, but also involves utilization of differentiation machinery as an additional elaboration mechanism. Each differentiation stage in both AML and B-cell ALL was characterized by utilization of cognate signaling networks which showed differential susceptibility to drug action. Using such deep profiling and signaling delineation approaches at the single-cell level will allow for fine structured indexing of patient disease and further tailoring of disease management. In addition, it will allow “heterogeneous” tumors to be organized by a maturation index associated with a granular catalog of mutations that drive cells to occupy these pseudo-differentiation niches. 1. Bendall, S.C., et al., A deep profiler's guide to cytometry.Trends Immunol, 2012. 33(7): p. 323-32. 2. Petilla Interneuron Nomenclature Group, et al., Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex.Nat Rev Neurosci, 2008. 9(7): p. 557-68. 3. Irish, J.M., et al., Single cell profiling of potentiated phospho-protein networks in cancer cells.Cell, 2004. 118(2): p. 217-28. 4. Sachs, K., et al., Causal protein-signaling networks derived from multiparameter single-cell data.Science, 2005. 308(5721): p. 523-9. 5. Majeti, R., C.Y. Park, and I.L. Weissman, Identification of a hierarchy of multipotent hematopoietic progenitors in human cord blood. Cell Stem Cell, 2007. 1(6): p. 635-45. 6. Tarnok, A., H. Ulrich, and J. Bocsi, Phenotypes of stem cells from diverse origin.Cytometry A, 2010. 77(1): p. 6-10. 7. O'Brien, C.A., A. Kreso, and J.E. Dick, Cancer stem cells in solid tumors: an overview.Semin Radiat Oncol, 2009. 19(2): p. 71-7. 8. Bandura, D.R., et al., Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry. Anal Chem, 2009. 81(16): p. 6813-22. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Low-Dose Interleukin-2 Therapy Activates Circulating T Follicular Regulatory Cells and Suppresses Circulating T Follicular Helper Cells in Patients with Chronic Gvhd

Blood ◽  
2017 ◽  
Vol 130 (Suppl_1) ◽  
pp. 74-74
Author(s):  
Yusuke Kamihara ◽  
Edouard Forcade ◽  
John Koreth ◽  
Hongye Liu ◽  
Tomohiro Kubo ◽  
...  
Keyword(s):  
B Cell ◽  
Single Cell ◽  
Board Of Directors ◽  
Research Funding ◽  
Low Dose ◽  
Cell Mass ◽  
Mass Cytometry ◽  
Advisory Committees ◽  
Healthy Donors

Abstract Introduction: T follicular helper (TFH) and T follicular regulatory (TFR) cells play important roles in the regulation of B-cell immunity. While TFH promote B cell functions in the germinal center (GC), TFR function as negative regulators of the GC response. Previous studies in murine models established that TFH and GC B cells are required for the development of chronic graft-versus-host disease (cGVHD). We previously reported that circulating TFH (cTFH) were more functionally activated in patients with active cGVHD compared with patients with no cGVHD. Low-dose IL-2 therapy has been shown to selectively expand CD4Treg and improve cGVHD symptoms. In the current study, we examined the effects of IL-2 therapy on cTFH and circulating TFR (cTFR) in patients with steroid resistant cGVHD. Methods: Single cell mass cytomtery (CyTOF) was performed on cryopreserved peripheral blood mononuclear cells (PBMC) from healthy donors and 17 adult patients with active cGVHD receiving daily low-dose IL-2 therapy (Koreth et al. Blood 2016). A panel of 35 metal-tagged monoclonal antibodies was used to simultaneously examine the phenotypic and functional effects of low-dose IL-2 on lymphocyte populations in vitro and in vivo. The analytic panel included 22 cell surface markers to identify distinct lymphocyte subsets and 13 intracellular markers to measure functional status and activation of specific signaling pathways. Before staining for surface and intracellular antigens, serial samples from individual patients were barcoded to ensure uniformity of analysis. viSNE was used to visualize of high-dimensional data on a two-dimensional map and quantify single cell mass cytometry data. Results: In PBMC from healthy donors, expression of CD25 (IL-2Rα), CD95, CTLA-4, BLIMP-1 and GITR was higher in cTFR compared with cTFH. To examine the response to IL-2 in vitro, PBMC from healthy donors were stimulated with IL-2 for 15 minutes (Figure 1A). At low IL-2 concentrations (1 to 10 IU/mL), phospho-STAT5 (p-STAT5) was selectively activated in cTFR compared with cTFH. At high IL-2 concentrations (100 to 1,000 IU/mL), p-STAT5 was activated in both cTFR and cTFH. To examine the response to IL-2 in vivo, we used mass cytometry to examine serial PBMC samples from cGVHD patients receiving daily low dose IL-2 therapy (1x106 IU/M2/day). Selective expansion of cTFR was noted after 1 week of treatment and cTFR expansion remained stable for the 12 week duration of therapy. Expanded cTFR increased expression of p-STAT5, FoxP3, BCL6, HLA-DR (Figure 1B) and CD25, CD95, CTLA-4, ICOS, Ki67 and Helios 1 week after starting IL-2. cTFR:cTFH ratio increased rapidly after starting low dose IL-2 and paralleled the increased Treg:Tcon ratio (Figure 1C). Activated TFH and TFR can be identified by expression of ICOS and PD-1. The expansion of ICOS+PD-1+ cTFR was evident after 1 week of IL-2 and remained elevated at the end of therapy. In contrast, ICOS+PD-1+ cTFH increased 1 week after starting IL-2 therapy but subsequently decreased and fell below baseline 6 and 12 weeks after starting IL-2 (Figure 1D). Activated ICOS+PD-1+ cTFR expressed higher levels of p-STAT5, BCL-6, FoxP3, HLA-DR and CD25 during low dose IL-2 therapy. In contrast, these functional markers were not increased in ICOS+PD-1+ cTFH during IL-2 therapy (Figure 1B). Conclusion: Single cell mass cytometry analysis revealed that daily low dose IL-2 therapy induces selective activation and increased expression of functional proteins in ICOS+PD-1+ cTFR. In contrast, activated ICOS+PD-1+ cTFH were suppressed during IL-2 therapy. The selective activation of cTFR and suppression of cTFH provide a mechanism whereby low dose IL-2 therapy can promote B cell tolerance as well as T cell tolerance in patients with cGVHD. Disclosures Forcade: Neovii: Other: Travel grant. Koreth: Amgen Inc.: Consultancy; Prometheus Labs: Research Funding; Kadmon Corp: Membership on an entity's Board of Directors or advisory committees; Millennium Pharmaceuticals: Research Funding; Takeda Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees. Nikiforow: Kite Therapeutics: Membership on an entity's Board of Directors or advisory committees. Armand: Infinity: Consultancy; Bristol-Myers Squibb: Consultancy, Research Funding; Otsuka: Research Funding; Tensha: Research Funding; Sequenta/Adaptive: Research Funding; Genmab: Consultancy; Affimed: Research Funding; Sigma Tau: Research Funding; Merck & Co., Inc.: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Roche: Research Funding. Cutler: Bristol-Myers Squibb: Consultancy; Pfizer: Consultancy; Kite: Consultancy; Pharmacyclics: Consultancy; Incyte: Consultancy; Astellas: Consultancy.

scholarly journals Multi-Dimensional Immuno-Profiling of Drosophila Hemocytes by Single Cell Mass Cytometry

2020 ◽  
Author(s):  
József Á. Balog ◽  
Viktor Honti ◽  
Éva Kurucz ◽  
Beáta Kari ◽  
László G. Puskás ◽  
...  
Keyword(s):  
Single Cell ◽  
Complex Analysis ◽  
Single Cells ◽  
Cell Mass ◽  
Antigen Expression ◽  
Bioinformatic Analysis ◽  
Multidimensional Analysis ◽  
Cell Mediated Immunity ◽  
Mass Cytometry ◽  
Cell Surface Antigens

AbstractSingle cell mass cytometry (SCMC) combines features of traditional flow cytometry (FACS) with mass spectrometry and allows the measurement of several parameters at the single cell level, thus permitting a complex analysis of biological regulatory mechanisms. We optimized this platform to analyze the cellular elements, the hemocytes, of the Drosophila innate immune system. We have metal-conjugated six antibodies against cell surface antigens (H2, H3, H18, L1, L4, P1), against two intracellular antigens (3A5, L2) and one anti-IgM for the detection of L6 surface antigen, as well as one anti-GFP for the detection of crystal cells in the immune induced samples. We investigated the antigen expression profile of single cells and hemocyte populations in naive, in immune induced states, in tumorous mutants (hopTum bearing a driver mutation and l(3)mbn1 carrying deficiency of a tumor suppressor) as well as in stem cell maintenance defective hdcΔ84 mutant larvae. Multidimensional analysis enabled the discrimination of the functionally different major hemocyte subsets, lamellocytes, plasmatocytes, crystal cell, and delineated the unique immunophenotype of the mutants. We have identified sub-populations of L2+/P1+ (l(3)mbn1), L2+/L4+/P1+ (hopTum) transitional phenotype cells in the tumorous strains and a sub-population of L4+/P1+ cells upon immune induction. Our results demonstrated for the first time, that mass cytometry, a recent single cell technology combined with multidimensional bioinformatic analysis represents a versatile and powerful tool to deeply analyze at protein level the regulation of cell mediated immunity of Drosophila.

scholarly journals Single-Cell Mass Cytometry Characterizes Phenotypic and Functional Heterogeneity in Acute Myeloid Leukemia at Diagnosis, in Remission and Relapse

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 912-912
Author(s):  
Lina Han ◽  
Peng Qiu ◽  
Jeffrey L. Jorgensen ◽  
Duncan Mak ◽  
Jared K Burks ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Single Cell ◽  
Intracellular Signaling ◽  
Myeloid Leukemia ◽  
Research Funding ◽  
Cell Mass ◽  
Mass Cytometry ◽  
Intracellular Signaling Pathways ◽  
Genetics Research ◽  
Acute Myeloid

Abstract Patients with acute myeloid leukemia (AML) continue facing poor long-term survival due to high relapse rate. Persistence of dormant self-renewing leukemia stem/progenitor cells (LSPCs) has been implicated as driver of subsequent relapse, and stem cell gene signatures are associated with poor outcome (Shlush et al. Nature 2017; Ng et al. Nature 2016; Eppert et al Nat Med 2011). Identification of the unique phenotypes and functional proteins in LSCs surviving induction therapy may aid in understanding the mechanisms of chemoresistance and provide novel therapeutic targets in the residual leukemia clones. In this study, we have developed and optimized a comprehensive single-cell mass cytometry (CyTOF) panel, including 36 markers to define LSPCs, with the goal to identify and characterize expression of multiple intracellular signaling pathways and anti-apoptotic proteins in residual AML cells. The validated CyTOF panel was applied in 21 samples collected from 7 AML patients at diagnosis, in remission and at relapse and 5 healthy donors. Data were analyzed using SPADE (Qiu et al, Nat Biotechnol 2011) or Cytofkit (Chen et al. PLoS Comput Biol 2016) tools. We first generated SPADE trees for all diagnostic samples (n=7, Fig 1A), annotating 7 distinct cell populations based on the median expression of selected surface markers as shown in the heatmap (Fig 1B). From these annotations, populations A1, A2 and A6 were positive for CD34 expression, with A6 representing phenotypically the most primitive fraction CD34hiCD38low population (frequency range 0.04%-17.32%). Fractions A1 and A2 expressed more committed myeloid markers, positive for CD135 and CD33 progenitor markers and differentiation markers including CD15, CD11b, and CD7 (Fig 1B). Variability was observed in terms of cell composition, and non-stem fractions A3-A5 were abundant populations in all AML samples except AML2. We further studied the activation/expression of functional proteins in these populations and found that pro-survival BCL-2 protein was highly expressed in the primitive A6 population across AML samples (median intensity 9.0 ± 5.3 in A6 vs 3.5 ± 3.9 in other populations). Variable p-AKT activation was observed in both A6 (4.9 ± 4.2) and differentiated A3-A5 populations (4.5 ± 3.6). We next examined how multi-parametric CyTOF analysis will aid in characterization of MRD populations by comparing samples from 7 patients collected at the time of diagnosis, in remission and at relapse. Using the Cytofkit bioconductor analysis and FlowSOM algorithm, we identified distinct patterns of relapse (Fig 1C). In AML 1-3, major populations were markedly reduced by induction chemotherapy, but residual cells re-grew and contributed to relapse. In AML 4-7, the major populations at diagnosis were eliminated by the therapy, but minor (or undetectable) populations at diagnosis progressed over treatment and represented the bulk of leukemia upon relapse. This finding is consistent with genomic studies that relapse may originate from either the founding clones or subclones that acquire additional mutations (Ding et al. Nature 2012). In AML#3, a major population (cluster 7, CD34−CD38+CD123+CD64+HLA-DR+CD99+, 75.6%) present at diagnosis was identified as persisting in remission at 2.5% by CyTOF analysis but not by conventional MRD flow cytometry and gave rise to the overt leukemia (78.1%) at relapse (Fig 1D). In this patient whose AML harbored mutation in negative MAPK regulator phosphatase PTPN11, persistent AML cells expressed BCL-2, MCL-1, and p-p38MAPK (Fig 1E), consistent with dominant activation of MAPK signaling and anti-apoptotic proteins. We found highly enriched BCL-2 expression and p38MAPK activation in relapse-driving clones in AML5 and AML7, and in diagnostic clone in AML6 (Fig 1F). In summary, using CyTOF, SPADE and Cytofkit analysis tools, we characterized LSPC-specific intracellular signaling pathways in AML samples at diagnosis, in remission and at the time of relapse. Distinct populations were identified to contribute to relapse, indicating that use of additional targeted therapies such as BCL-2 inhibitors may be instrumental post remission to prevent relapse. In conclusion, analysis of the multi-parametric single cell CyTOF mass cytometry may aid in understanding clonal evolution during chemotherapy and identify potential therapeutic targets in individual patients. Disclosures Ravandi: Astellas Pharmaceuticals: Consultancy, Honoraria; Xencor: Research Funding; Abbvie: Research Funding; Amgen: Honoraria, Research Funding, Speakers Bureau; Bristol-Myers Squibb: Research Funding; Abbvie: Research Funding; Jazz: Honoraria; Seattle Genetics: Research Funding; Bristol-Myers Squibb: Research Funding; Macrogenix: Honoraria, Research Funding; Amgen: Honoraria, Research Funding, Speakers Bureau; Xencor: Research Funding; Astellas Pharmaceuticals: Consultancy, Honoraria; Orsenix: Honoraria; Jazz: Honoraria; Orsenix: Honoraria; Sunesis: Honoraria; Sunesis: Honoraria; Seattle Genetics: Research Funding; Macrogenix: Honoraria, Research Funding. Roboz:Roche/Genentech: Consultancy; Pfizer: Consultancy; Bayer: Consultancy; Celgene Corporation: Consultancy; Astex Pharmaceuticals: Consultancy; Argenx: Consultancy; Roche/Genentech: Consultancy; Janssen Pharmaceuticals: Consultancy; Janssen Pharmaceuticals: Consultancy; Pfizer: Consultancy; Eisai: Consultancy; Jazz Pharmaceuticals: Consultancy; Orsenix: Consultancy; Astex Pharmaceuticals: Consultancy; Aphivena Therapeutics: Consultancy; Celgene Corporation: Consultancy; Sandoz: Consultancy; Novartis: Consultancy; Bayer: Consultancy; Otsuka: Consultancy; Aphivena Therapeutics: Consultancy; Celltrion: Consultancy; Daiichi Sankyo: Consultancy; Sandoz: Consultancy; Daiichi Sankyo: Consultancy; Jazz Pharmaceuticals: Consultancy; Celltrion: Consultancy; AbbVie: Consultancy; AbbVie: Consultancy; Orsenix: Consultancy; Cellectis: Research Funding; Novartis: Consultancy; Cellectis: Research Funding; Argenx: Consultancy; Eisai: Consultancy; Otsuka: Consultancy. Andreeff:AstraZeneca: Research Funding. Guzman:Cellectis: Research Funding. Konopleva:Stemline Therapeutics: Research Funding.

scholarly journals Single-Cell Transcriptional Landscapes of Human Bone Marrow Reveal Distinct Erythroid Phenotypes Underpinned By Genotype in Diamond-Blackfan Anemia

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 1-2
Author(s):  
Deena Iskander ◽  
Guanlin Wang ◽  
Elisabeth F Heuston ◽  
Chrysi Christodoulidou ◽  
Bethan Psaila ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Single Cell ◽  
Research Funding ◽  
Molecular Mechanisms ◽  
Ex Vivo ◽  
Single Cells ◽  
Developmental Trajectory ◽  
Gene Set Enrichment Analysis ◽  
Hematopoietic Stem ◽  
Diamond Blackfan Anemia

Background: Diamond- Blackfan Anemia (DBA) is a rare, heritable ribosomopathy caused by mutations in ribosomal protein large (RPL) and small (RPS) subunit genes. The diagnostic criteria of DBA include presentation in infancy with virtually no mature erythroblasts (EB) on bone marrow (BM) examination, however atypical presentations in later life with milder haematological manifestations are increasingly reported. The cellular and molecular mechanisms underpinning variable clinical phenotypes, and how they relate to genotype, are yet to be elucidated. Furthermore, as many DBA patients do not respond to corticosteroids, understanding the pathological processes contributing to erythroid failure is a prerequisite for developing novel, precision-medicine therapies. Aim: To elucidate phenotypic and functional differences in erythropoiesis in RPS- and RPL-DBA, using primary BM samples from patients. Methods: We performed single-cell transcriptome profiling (scRNAseq), using the 10X Genomics chromium platform, of 45,488 CD34+ Lineage negative (Lin-) BM hematopoietic stem and progenitor cells (HSPC), purified by fluorescence-activated cell sorting. We included six patients with red cell transfusion-dependent DBA (aged 2-19y) with mutations in RPS19 (n=3), RPL11 (n=1) and RPL5 (n=2) and three healthy donors (aged 3-17y). We validated our findings using bulk RNAseq, functional assays, and deep immunophenotyping-based dissection of the haematopoietic architecture of DBA BM ex vivo. Results: High quality sequencing data was obtained for all nine donors; after quality control, 41,415 single cells were carried forward for analysis. Unsupervised clustering analysis and lineage identification revealed two divergent cellular patterns in DBA compared with age-matched control BM that segregated with genotype: a selective loss of erythromegakaryocyte (E/MK) progenitors in RPS-DBA, but relative preservation of the erythroid developmental trajectory in RPL-DBA, at the expense of megakaryopoiesis (Fig 1a). Gene Set Enrichment Analysis (GSEA) of differentially expressed genes between control and DBA HSPC clusters revealed p53-mediated apoptosis, TNFa-, IFNa- and IFNg-mediated inflammatory pathways in DBA EP. Although these pathways were enriched across all HSPC populations irrespective of genotype, they were detected at an earlier stage of the stem cell hierarchy, and more potently, in RPS- versus RPL-EP. Expression of transcriptional targets of the master E/MK transcription factor, GATA1, was significantly upregulated in RPL- versus RPS-DBA EP (Fig 1b), supporting the maintained erythroid program in RPL-DBA HSPC. However, expression of genes denoting erythroid differentiation, including adult haemoglobin (Hb) genes, was significantly elevated in RPL-DBA, suggesting aberrant accelerated differentiation to EB. These findings were confirmed by immunophenotypic examination of patient BM and bulk RNAseq of RPL-EB. Additionally, single-cell clonogenic assays of RPL-DBA EP showed severe qualitative defects. Thus, although erythroid commitment occurs in RPL-DBA, it is coupled with accelerated maturation beyond EP to functionally impaired mature EB, enriched in inflammatory and p53 pathways. Finally, we analysed the clinical characteristics of the 161 patients comprising the U.K DBA registry. In line with the milder erythroid specification defect, patients with RPL-DBA (n=44) presented with anaemia later (P=0.004), and with a higher average Hb concentration (P=0.04), than those with RPS-DBA (n=63). Furthermore, we identified higher corticosteroid responses in RPL-DBA, assessed at 6 months post initiation (P=0.006), consistent with our findings in RPL-DBA of preservation of the cellular EP populations that are targeted by corticosteroids. Impact: In conclusion, a preserved but distinct erythroid developmental trajectory, characterised by accelerated differentiation, underpins a milder haematological phenotype in RPL-DBA. Furthermore, we reveal the first single-cell transcriptomic dataset from haematopoietic cells in a ribosomopathy, uncovering novel cell intrinsic and extrinsic pathogenetic insights into failing erythropoiesis in DBA. Integration of these data with clinical genomics and phenomics provides a paradigm by which single cell approaches can be used to decipher genotype-phenotype relationships in Mendelian genetic disorders. Disclosures Mead: Novartis: Consultancy, Honoraria, Other: travel, accommodations, expenses, Research Funding, Speakers Bureau; Celgene/BMS: Consultancy, Honoraria, Other: travel, accommodations, expenses, Research Funding; Abbvie: Consultancy; CTI: Consultancy; Gilead: Consultancy.

scholarly journals Single Cell Mass Cytometry Reveals Hyperactivated Signaling Networks in Myeloproliferative Neoplasms

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 1884-1884
Author(s):  
Daniel A.C. Fisher ◽  
Olga Malkova ◽  
Mary C. Fulbright ◽  
Gregory K. Behbehani ◽  
Garry P. Nolan ◽  
...  
Keyword(s):  
Single Cell ◽  
Signaling Pathways ◽  
Intracellular Signaling ◽  
Colony Formation ◽  
Myeloproliferative Neoplasms ◽  
Cell Mass ◽  
Innovative Technology ◽  
Signaling Networks ◽  
Mass Cytometry ◽  
Hematopoietic Stem

Abstract Myeloproliferative neoplasms (MPNs) including myelofibrosis (MF) are characterized by chronic hyperactivation of a signaling axis downstream of the JAK2 kinase. Pharmacologic inhibitors of JAK2 ameliorate constitutional symptoms and splenomegaly in MF patients. However, these agents do not appear to be capable of eradicating the malignant clone, nor have they have been shown to prevent transformation to secondary acute myeloid leukemia (sAML). These findings suggest that aberrant activation of additional signaling pathways, either downstream of JAK2, or via alternative mechanisms, may contribute to MPN pathogenesis. To develop more effective therapeutic strategies, a fuller understanding of these altered signaling pathways in MPNs is needed. Mass cytometry is an innovative technology that enables the characterization of dysregulated signaling networks at the single cell level. We utilized this approach to examine intracellular signaling phenotypes of seven MF patients, five sAML patients, and five normal controls across two independent experiments. Patient CD34+ hematopoietic stem and progenitor cells (HSPCs) frequently exhibited basal (unstimulated) signaling abnormalities suggestive of chronic hyperactivation of the JAK-STAT, MAP kinase/PI3 kinase, and NFκB signaling pathways. HSPCs from individual patients also exhibited hypersensitive responses to stimulation by the cytokines thrombopoietin (TPO), G-CSF, and/or TNFα. Elevated phosphorylation of the signaling molecules AKT, ERK, CREB, and S6 suggests an extensive network of hyperactivated signaling in MF and sAML HSPCs. Evidence of NFκB signaling hyperactivation was identified as indicated by (1) elevated phosphorylation of the NFκB subunit p65/RELA and supranormal abundance of IκBα in unstimulated cells; and (2) hypersensitive responses to TNFα, in the form of TNFα stimulated p65/RELA phosphorylation and IκBα degradation. Pronounced NFκB signaling hyperactivation was observed in a subset of MF and sAML patients from these experiments. Elevated NFκB signaling was predominantly insensitive to ex vivo exposure to the JAK inhibitor ruxolitinib, but was partly sensitive to the IκB kinase inhibitor IKKiVII. The relevance of NFκB signaling to myeloproliferation was tested by colony-forming unit (CFU) assays with MF patient HSPCs. IKKiVII inhibited myeloid colony formation from MF CD34+ cells with a potency similar to that observed for ruxolitinib. Inhibition of colony formation by IKKiVII was enhanced in combination with ruxolitinib. Similarly, growth of the JAK2 mutant HEL cell line was inhibited by IKKiVII with a potency similar to that observed for ruxolitinib, and the combination of IKKiVII and ruxolitinib gave substantially greater inhibition than either inhibitor alone. This suggests that NFκB signaling may be an important component of myeloproliferation, particularly in the context of hyperactive JAK2. These findings suggest that co-targeting of JAK2 and NFκB could be beneficial therapeutically. Ongoing experiments are focused on further characterizing the extent of dysregulated signaling in MPNs, as well as the prevalence of hyperactive NFκB signaling in MF and sAML. These experiments will identify components of myeloproliferative signaling which are abnormally active in MF and sAML, and may represent targets for improved therapeutic intervention. Disclosures Oh: Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding.

scholarly journals DGCyTOF:  deep learning with graphic cluster visualization to predict cell types of single cell mass cytometry data

2021 ◽  
Author(s):  
Lijun Cheng ◽  
Pratik Karkhanis ◽  
Birkan Gokbag ◽  
Lang Li
Keyword(s):  
Deep Learning ◽  
Single Cell ◽  
3D Visualization ◽  
Single Cells ◽  
Cell Mass ◽  
Cell Types ◽  
Learning System ◽  
Cell Populations ◽  
Calibration System ◽  
Mass Cytometry

Background :  Single-cell mass cytometry, also known as cytometry by time of flight (CyTOF) is a powerful high-throughput technology that allows analysis of up to 50 protein markers per cell for the quantification and classification of single cells. Traditional manual gating utilized to identify new cell populations has been inadequate, inefficient, unreliable, and difficult to use, and no algorithms to identify both calibration and new cell populations has been well established. Methods :   A deep learning with graphic cluster (DGCyTOF) visualization is developed as a new integrated embedding visualization approach in identifying canonical and new cell types. The DGCyTOF combines deep-learning classification and hierarchical stable-clustering methods to sequentially build a tri-layer construct for known cell types and the identification of new cell types. First, deep classification learning is constructed to distinguish calibration cell populations from all cells by softmax classification assignment under a probability threshold, and graph embedding clustering is then used to identify new cell populations sequentially. In the middle of two-layer, cell labels are automatically adjusted between new and unknown cell populations via a feedback loop using an iteration calibration system to reduce the rate of error in the identification of cell types, and a 3-dimensional (3D) visualization platform is finally developed to display the cell clusters with all cell-population types annotated. Results : Utilizing two benchmark CyTOF databases comprising up to 43 million cells, we compared accuracy and speed in the identification of cell types among DGCyTOF, DeepCyTOF, and other technologies including dimension reduction with clustering, including Principal Component Analysis ( PCA ) , Factor Analysis ( FA ), Independent Component Analysis ( ICA ), Isometric Feature Mapping ( Isomap ), t-distributed Stochastic Neighbor Embedding ( t-SNE ), and Uniform Manifold Approximation and Projection ( UMAP ) with k -means clustering and Gaussian mixture clustering. We observed the DGCyTOF represents a robust complete learning system with high accuracy, speed and visualization by eight measurement criteria. The DGCyTOF displayed F-scores of 0.9921 for CyTOF1 and 0.9992 for CyTOF2 datasets, whereas those scores were only 0.507 and 0.529 for the t-SNE + k-means ; 0.565 and 0.59, for UMAP + k-means . Comparison of DGCyTOF with t-SNE and UMAP visualization in accuracy demonstrated its approximately 35% superiority in predicting cell types. In addition, observation of cell-population distribution was more intuitive in the 3D visualization in DGCyTOF than t-SNE and UMAP visualization. Conclusions :  The DGCyTOF model can automatically assign known labels to single cells with high accuracy using deep-learning classification assembling with traditional graph-clustering and dimension-reduction strategies. Guided by a calibration system, the model seeks optimal accuracy balance among calibration cell populations and unknown cell types, yielding a complete and robust learning system that is highly accurate in the identification of cell populations compared to results using other methods in the analysis of single-cell CyTOF data. Application of the DGCyTOF method to identify cell populations could be extended to the analysis of single-cell RNASeq data and other omics data.

scholarly journals Single-Cell Mass Cytometry Identifies Mechanisms of Resistance to Immunotherapy in AML

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1428-1428
Author(s):  
Shelley Herbrich ◽  
Antonio Cavazos ◽  
Cora Marie C Cheung ◽  
Lynette Alexander-Williams ◽  
Nicholas J. Short ◽  
...  
Keyword(s):  
Bone Marrow ◽  
T Cells ◽  
Single Cell ◽  
Research Funding ◽  
Cell Mass ◽  
Response To Therapy ◽  
Hypomethylating Agents ◽  
Mass Cytometry ◽  
Mechanisms Of Resistance ◽  
Cd8 Cells

Background: The mechanisms of resistance to immunotherapies in patients with acute myeloid leukemia (AML) are not well characterized and biomarkers for improved immunotherapeutic strategies are critical. Multiple phase II/III clinical trials combining hypomethylating agents (HMA), azacitidine (AZA) and decitabine, with immune checkpoint inhibitors are now underway for patients with newly diagnosed and relapsed/refractory (R/R) AML and MDS based on data suggesting that 1) hypomethylating agents increase tumor expression of PD-L1 and PD-1 in myeloid malignancies(Yang H et al. Leukemia 2014); 2) blocking the PD-1/PD-L1 signaling axis has profound anti-leukemic response (Zhou et al. Blood 2011); and 3) clinical responses to Azacitidine/PD1 inhibitor (nivolumab) in relapsed AML (Daver et al., Cancer Discovery). Here, we characterize the baseline immune landscape and potential mechanisms of resistance using single-cell mass cytometry (CyTOF) profiling of serially collected samples from R/R AML patients undergoing therapy with AZA and PD-L1 inhibitor avelumab [NCT02953561]. Methods: Bone marrow (BM) and peripheral blood (PB) samples were collected from 9 patients prior to treatment with AZA and avelumab and after cycles 1, 3, and 5 (as available). To interrogate immune profiling in these samples, we have developed and optimized a novel CyTOF antibody panel that includes immunophenotypic markers to distinguish AML stem cells and blasts from adjacent immune cell subsets (T, B, NK cells, and monocytes), as well as known checkpoint ligands and receptors. Data was analyzed using the uniform manifold approximation and projection (UMAP) algorithm (McInnes et al. arXiv 2018) implemented through Cytofkit (Chen et al. PLoS Comput Biol 2016). Results: We first used the multi-parameter immunophenotyping to characterize the immune microenvironment in normal and leukemic BM prior to therapy. At baseline, the composition of AML BM CD4 and CD8 T cells contained a significantly smaller fraction of naïve T-cells when compared to healthy bone marrow controls (both p<0.001). The proportion of terminally differentiated CD8+ cells was also significantly less in AML BM (p<0.001). Conversely, effector memory CD4 and CD8 cells comprised the major fraction of T-cells in AML BMs (Fig 1D). Due to the small number of patients we investigated response to therapy on a cycle-by-cycle basis rather than overall patients' outcomes. By this approach, we found that the relative ratio of T cells to blasts did not significantly predict response, however, patients with blast reduction or stable disease after 1 cycle of treatment had significantly lower CD4:CD8 ratios (i.e. more CD8 cells per CD4 cell) than those who progressed (p=0.04). In fact, CD4:CD8 ratio at the beginning of the cycle was significantly correlated with relative blast reduction in the bone marrow (R2=0.25, p=0.035) (Fig 1E). This is consistent with what has been seen in other cancer types where low CD4:CD8 has been associated with favorable prognosis (Sato et al. PNAS 2005). Baseline PD-L1 expression levels in these patient samples was low and did not predict response to therapy. While 4 of the 9 patients experienced and initial blast reduction, 7 developed disease progression while on the trial. Serial measurements from the same patients allowed us to track both resistant and newly emerging clones over the course of therapy (representative patient; Fig 1A-C). While PD-L1 levels were consistently low, we did observe high PD-L2 protein expression in AML cells resistant (the 7 with progressive disease) to HMA/PD-L1 inhibition, and PD-L2 was also frequently expressed in the emerging clones not present at baseline. We profiled the AML clones from each patient present at disease progression for the expression of other known checkpoint ligands and receptors (Fig 1F). Expression of PD-L2, OX40, and TIM-3 was detected in the majority of these resistant clones (100%, 86%, and 71%, respectively). Conclusions: Single-cell characterization of R/R AML reveals that the immune landscape of these patients at baseline is significantly altered when compared to healthy bone marrow. The ratio of CD:/CD8 and composition of residual T cells appear to be the most important predictors of response to HMA/PD-L1 inhibition. Finally, AML cells express a variety of other immune checkpoints, particularly PD-L2 but also OX40 and TIM3, that should be considered for future combination therapy. Disclosures Short: AstraZeneca: Consultancy; Amgen: Honoraria; Takeda Oncology: Consultancy, Research Funding. Konopleva:Astra Zeneca: Research Funding; Reata Pharmaceuticals: Equity Ownership, Patents & Royalties; Ablynx: Research Funding; Ascentage: Research Funding; Kisoji: Consultancy, Honoraria; Genentech: Honoraria, Research Funding; Amgen: Consultancy, Honoraria; F. Hoffman La-Roche: Consultancy, Honoraria, Research Funding; Cellectis: Research Funding; Eli Lilly: Research Funding; AbbVie: Consultancy, Honoraria, Research Funding; Stemline Therapeutics: Consultancy, Honoraria, Research Funding; Forty-Seven: Consultancy, Honoraria; Calithera: Research Funding; Agios: Research Funding.

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